A Framework for Quantifying Carbon Emissions Generated During Demolition Waste Processing

The standard of living has significantly risen along with ongoing economic progress, but CO2 emissions have also been rising. The reduction in CO2 resulting from the daily activities of residents has become a crucial priority for every province. A relevant study on the carbon emissions of Hebei Province residents was conducted for this publication, aiming to provide a theoretical basis for the sustainable development of Hebei Province. The first part of the article calculates the carbon emissions of Hebei Province people from 2005 to 2020 using the emission factor method and the Consumer Lifestyle Approach (CLA). Secondly, the Logarithmic Mean Divisia Index (LMDI) decomposition approach is used to assess the components that influence both direct and indirect carbon emissions. Finally, the scenario analysis approach is employed in conjunction with the LEAP model to establish baseline, low-carbon, and ultra-low-carbon scenarios to predict the trend of residents’ carbon emissions in Hebei Province from 2021 to 2040. The results show that the total carbon emissions of residents in Hebei Province from 2005 to 2020 rose, from 77.45 million tons to 153.35 million tons. Income level, energy consumption intensity, and population scale are factors that contribute to the increase in direct carbon emissions, while consumption tendency factors have a mitigating effect on direct carbon emissions. Economic level, consumption structure, and population scale factors are factors that contribute to the increase in indirect carbon emissions, while energy consumption intensity and energy structure factors have a mitigating effect on indirect carbon emissions. The prediction results show that under the baseline scenario, the cumulative residents’ carbon emissions in Hebei Province will not reach a zenith from 2021 to 2040. However, under the low-carbon situation, the carbon emissions of residents in Hebei Province will peak in 2029, with a peak of 174.69 million tons, whereas under the ultra-low-carbon scenario, it will peak in 2028, with a peak of 173.27 million tons.

The impacts of household structure transitions on household carbon emissions in China

Carbon emission has become a global focus. The construction of carbon emissions calculation model is helpful for its control. Currently, there is still no uniform method about accounting on the carbon emissions of steel products. The common calculation models are not totally suitable for China. To make up for the shortcomings of them, this paper defines the life cycle system of the iron and steel products based on EIO-LCA, measures the quantity of the direct, indirect carbon emissions and carbon emission deduction in various stages of this life cycle, identifies the hotspot and department which contributes most in carbon emission, and takes Hunan Valin Xiangtan Iron and Steel Co., Ltd (abbreviated Xiang Gang) as an example to validate it. It shows that 2103.87kg of carbon in total would be emitted when one tonne of steel is produced by Xiang Gang. Among the total, the quantity of direct, indirect and deductible carbon emission are 2033.5kg, 216.75kg and 146.38kg respectively, namely carbon emissions of producing per ton of steel is 2.1 tons. Direct carbon emissions from all stages of the life cycle of steel products mainly exist in the stage of steel production and transportation. And ferrous metal smelting and rolling processing industry are the largest emissions industries of the total indirect emissions. Converting by-product gas, heat, and pressure into electrical energy use can reduce carbon dioxide emissions by 146kg, which is the equivalent of reducing carbon dioxide emissions per ton of steel 0.15 tons. Therefore, in order to make the carbon dioxide emissions reach the advanced domestic level of 1.7 tons per ton steel, the iron and steel enterprises can meet emissions reduction targets by strengthening control of carbon emission and improving the efficiency of the utilization of secondary energy from small and large scale.

Indirect carbon emissions caused by residential consumption has gradually become the key to the formulation of carbon emission reduction policies. In order to analyze the factors that influence the provincial residential indirect carbon emissions in China, comprehensive structural decomposition analysis (SDA) and logarithmic mean Divisia index (LMDI) models are established in this paper. The Liaoning province was selected due to its typical features as a province with higher urbanization rates. The model is based on input–output tables from 2002 to 2012, including those pertaining to the carbon emission coefficient (ΔF), energy intensity effect (ΔE), intermediate demand (ΔL), commodity structure (ΔS), residential consumption structure (ΔU), residential consumption ratio (ΔR), per capita GDP (ΔA) and population size (ΔP). The results show that the consumption of urban residents is the most common and significant section causing the growth of direct and indirect carbon emissions, both of which show an obvious upward trend. Nonmetal mining is the sector experiencing the greatest growth in indirect carbon emissions. The two most influential factors of indirect carbon emissions via the consumption of rural and urban residents are the intermediate demand effect (ΔL) and the per capita GDP effect (ΔA), respectively. Reducing energy intensity and optimizing commodity structures are the most effective ways to reduce indirect carbon emissions.

Some problems exist in the current carbon emissions benchmark setting systems. The primary consideration for industrial carbon emissions standards highly relate to direct carbon emissions (power-related emissions) and only a portion of indirect emissions are considered in the current carbon emissions accounting processes. This practice is insufficient and may cause double counting to some extent due to mixed emission sources. To better integrate and quantify direct and indirect carbon emissions, an embodied industrial carbon emissions benchmark setting method is proposed to guide the establishment of carbon emissions benchmarks based on input-output analysis. This method attempts to link direct carbon emissions with inter-industrial economic exchanges and systematically quantifies carbon emissions embodied in total product delivery chains. The purpose of this study is to design a practical new set of embodied intensity-based benchmarks for both direct and indirect carbon emissions. Beijing, at the first level of carbon emissions trading pilot schemes in China, plays a significant role in the establishment of these schemes and is chosen as an example in this study. The newly proposed method tends to relate emissions directly to each responsibility in a practical way through the measurement of complex production and supply chains and reduce carbon emissions from their original sources. This method is expected to be developed under uncertain internal and external contexts and is further expected to be generalized to guide the establishment of industrial benchmarks for carbon emissions trading schemes in China and other countries.

Structural decomposition analysis of carbon emissions from residential consumption in the Beijing-Tianjin-Hebei region, China

Hydropower is the largest renewable source of electricity generation, the carbon emissions of which have attracted a lot attention. However, the system boundaries of existing studies are either incomplete or inaccurate. Therefore, this study provides a systems accounting framework for evaluating both the direct and indirect carbon emissions from a hydropower plant. It is based on the hybrid method as a combination of the process analysis and the input-output analysis. To demonstrate the framework, a case study for a typical pumped storage hydropower plant (NPSHP) is carried out. The total carbon emissions are estimated as 5828.39 kt in the life-cycle of the case system. The end-of-use stage causes the largest carbon emissions (38.4%), followed by the construction stage (34.5%), the operation stage (25.6%), and the preparation stage (1.5%). The direct carbon emissions are mainly released from sediments in the end-of-use stage and the surface of reservoirs in the operation stage (94.8%). The indirect carbon emissions are 2.8 times higher than the direct carbon emissions. The material, machinery, energy, and service inputs respectively account for 7.1%, 14.7%, 15.9%, and 62.3% of the total indirect carbon emissions by the case system. The indicator of EGOC (electricity generation on carbon emission) for the NPSHP is calculated as 26.06 g CO2-eq./kWh, which is lower than that of most other power plants.

Carbon emissions of prefabricated steel structure components: A case study in China

Agri-food evolution and carbon emissions in Chinese residential consumption: A life cycle analysis of urban-rural disparities and socioeconomic influences

Sewage treatment systems are a major source of urban carbon emissions. As sewage discharge standards increase, identifying the impact of upgrading sewage treatment plants on carbon emissions is essential. In this study, a sewage treatment plant with the typical anaerobic/anoxic/aerobic (A2/O) biological treatment process in Xi'an City was selected. Based on the IPCC National Greenhouse Gas Inventory Guide and carbon emission factor method, the carbon emissions of the sewage treatment system before and after upgrading were estimated. The results showed that the removal rates of COD, BOD5, NH3-N, and TP reached 95% or more after upgrading the wastewater treatment plant. The sewage treatment system's annual carbon emissions (measured by CO2-eq, the same below) increased by 15 065.65 t, the carbon emissions per ton of water increased by 0.103 kg, and the cost per ton of water treatment increased by 0.14 yuan. The direct carbon emission intensity of the sewage treatment was 0.083 kg·m-3, which increased by 9.19% compared with that before the transformation. The indirect carbon emission intensity was 0.412 kg·m-3, which increased by 25.8% compared with that before the transformation. Direct and indirect carbon emissions from the sewage treatment accounted for 16.82% and 83.18%, respectively. The direct carbon emission intensity of the sludge treatment was 0.246 kg·m-3, which increased by 4.98% compared with that before the transformation. The indirect carbon emission intensity was 0.004 7 kg·m-3, which increased by 4.73% compared to that before the transformation. Direct and indirect carbon emissions from the sludge treatment accounted for 98.11% and 1.89%, respectively. The purpose of carbon reduction in sewage treatment plants can be achieved by establishing photovoltaic power stations in sewage treatment plants, controlling the concentration of DO and NO2--N within a certain range, or establishing sludge digestion ponds and using of biogas cogeneration.

In recent years, extreme weather disasters have occurred frequently, so reducing greenhouse gas emissions is an urgent task. How to accurately estimate corporate carbon emissions in real-time directly affects this task. Therefore, a real-time estimation framework of corporate carbon emissions based on load identification is proposed in this paper. In the proposed framework, the total carbon emission consists of two parts: direct carbon emission and indirect carbon emission. First, a Convolutional Neural Network - Bidirectional Long Short-Term Memory (CNN-BLSTM) model is presented and employed to monitor the states of devices in a factory in realtime. Then, the direct carbon emission is estimated according to the state and related carbon emission intensity. Meanwhile, the indirect carbon emission can be obtained through multiplying the marginal carbon emission factor by the electricity consumption in the factory. The proposed framework was used in a case study to estimate the carbon emission of a steel plant in real-time, which proved its effectiveness and accuracy.

Comprehensive and accurate carbon measurement in industrial parks is an important prerequisite for grasping the current status of carbon emissions in industrial parks and exploring their carbon reduction potential. In industrial parks, a large amount of indirect or direct carbon emissions will be generated in common processes such as power use, fuel consumption, and industrial processes, and how to realize the real-time and accurate measurement of indirect and direct carbon emissions in industrial parks will become the key foundation for promoting carbon reduction and emission reduction in industrial parks. This paper focuses on the impact of auxiliary services provided by distributed power generation on carbon emissions and the integration of multi-energy carbon emission measurement technology to carry out carbon emission accurate measurement technology research, puts forward an accurate measurement method of indirect electricity carbon emission in industrial parks considering auxiliary services, and forms a carbon emission measurement technology solution in line with the new energy power generation and multiple energy use in industrial parks to provide data support for the low-carbon transformation of electric power in the parks. The study will provide data support for the low-carbon transformation of electric power and energy in the industrial park.

Household consumption has become an important field of carbon dioxide emissions. Urban–rural disparity in the household carbon emissions (HCEs) of residents and their influencing factors are relevant to HCE reduction. Taking Fujian as the study area, the LMDI and SDA models were used to analyze the effects of influencing factors for the direct household carbon emissions (DHCEs) and indirect carbon emissions (IHCEs) of urban and rural residents from 2006 to 2018. The HCEs continue to rise, approximately 65% from the IHCEs in 2017, and urban areas occupied 67% in 2018. The gap between urban and rural per capita HCEs is narrowing. In 2017, approximately 75% of urban per capita HCEs came from the IHCEs, while the per capita DHCEs’ occupation exceeded the IHCEs in rural areas. Per capita consumption expenditure has the largest positive effect on the DHCEs and IHCEs in urban and rural areas. With the urbanization process, the inhibition effect of rural DHCEs is larger than the positive effect of the urban DHCEs, while the positive impact on urban areas is more substantial than on rural areas in the IHCEs. Combined with regional differences, urban and rural areas should take “common but differentiated” emission reduction responsibilities.

Because of increasing concern about global climate change,there has been a growing research interest in carbon footprint analysis recent years.Carbon footprint analysis on urban energy use takes both direct carbon emissions and indirect emissions into account,so it has great significance for the in-depth analysis on nature process of carbon emissions and scientific formulation on carbon reduction plan.Taking Xiamen as a study area,the hybrid analysis method of carbon footprint was used to access the carbon footprint on the energy use of Xiamen city in 2009.Besides the direct carbon emissions from the urban energy end-use in traditional research,the indirect carbon emissions from the cross-boundary traffic and the embodied energy of key urban materials were also included.The direct emissions include emissions from direct energy use in industrial sector,commercial sector,household sector,transport sector and so on,which often called scope 1 by WRI/WBCSD.The direct emissions also include emissions due to electricity and steam purchases for the sector in the city,often called scope 2 emissions.And indirect carbon emissions,which called scope 3,incorporate the surface,sailing and airline travel′s emissions across cities and the embodied energy of key urban materials: food,water,fuel,and concrete.Research result showed that:(1) Direct carbon emissions from the energy use on the sectors inside the city boundary including industry,transport,commerce and so on,namely scope 1 and scope 2,only take up 64% of the total city carbon footprint.However,the indirect emissions from the cross-boundary traffic and embodied energy of key urban materials account for 36%,which has usually been ignored as scope 3.(2) In the direct emissions,the carbon emissions of industry contributed the largest share,which counts for 55%.And the emissions from the chemical industry account for 25% of the whole industry sector.(3) In the indirect emissions,the shares of emissions from cross-boundary traffic and embodied energy of key urban materials were 27% and 73% respectively.The carbon emissions of long-distance road transport take the largest proportion of cross-boundary traffic,which accounted for 38%.And the embodied energy emissions from the fuel were the most important part of embodied energy emissions,accounting for 51% of embodied emissions.(4) From the perspective of per capital carbon footprint,the per capital direct carbon emissions of Xiamen and Denver were 5.74 t CO2e/cap and 18.9 t CO2e/cap respectively.And the per capital carbon footprint of Xiamen and Denver,including direct and indirect emissions caused by the energy use,were 9.01 t CO2e/cap and 25.3 t CO2e/cap.In the total carbon footprint by Xiamen and Denver,the emissions from the cross-boundary transport all took up 10%,and the emissions caused by embodied energy of key urban materials were 26% and 15% respectively.The embodied emissions by Xiamen were relatively higher than Denver because the urbanization and industrialization consumed more materials in Xiamen.Comparing the per capital carbon emission excluding embodied emissions with typical cities in the worldwide,Denver took the first place with 21.5 t CO2e/cap,and Los Angeles,New York City,London,Bangkok,Cape Town and big city like Shanghai,Tianjin and Beijing in China were more than 10 t CO2e/cap averagely.That of Xiamen was 6.63 t CO2e/cap,which was far less than most cities.

The accelerated growth of the global economy has given rise to a multitude of environmental concerns that demand immediate attention. At this juncture, the total global carbon emissions are exhibiting a gradual increase. China, the United States, India, Russia, and Japan represent the top five countries in terms of global carbon emissions, collectively accounting for approximately 60% of the global total. Of these, China’s carbon emissions are the highest in the world, representing over 30% of the global total. As urbanization accelerates, the carbon emissions from urban agglomerations constitute a substantial share of the nation’s total emissions, rendering the carbon emissions of urban clusters a critical issue. In the context of China’s urban agglomerations, the Beijing–Tianjin–Hebei region, due to factors such as industrial structure, accounts for a relatively high proportion of carbon emissions, approximately 11% of the national total. The future trajectory of carbon emissions in the Beijing–Tianjin–Hebei region will significantly impact the high-quality development of the entire urban cluster. Consequently, research on carbon emissions in the Beijing–Tianjin–Hebei region is of vital importance. This paper takes the carbon emissions of the power industry in the Beijing–Tianjin–Hebei region as the research subject, analyzes its carbon emissions status, and builds a multi-regional input–output model for the Beijing–Tianjin–Hebei region based on the input–output tables and carbon emissions data of each province. This study explores the key influencing factors of carbon emissions from the power industry in this region from 2012 to 2017 and analyzes the carbon emissions transfer and structural evolution from the perspective of the region and the industry to clarify the carbon reduction responsibilities of the Beijing–Tianjin–Hebei region and provide references and recommendations for the formulation of regional collaborative emission reduction policies. The results show that the direct carbon emissions from the power industry in the Beijing–Tianjin–Hebei region account for a higher proportion compared to the indirect carbon emissions it generates by driving other industries. Industries with relatively high indirect carbon emissions in the key path include coal mining and selection, equipment manufacturing, transportation, services, etc. The capital input process from Tianjin and Hebei to Beijing is accompanied by a relatively high carbon transfer. Promoting the widespread adoption of carbon emission reduction technologies will have an effective suppressive effect on carbon emissions in the Beijing–Tianjin–Hebei region, especially in Hebei; Beijing and Tianjin should pay attention to the stimulating effect of increased final demand on carbon emissions; the transfer of carbon emissions between regions and industries shows a downward trend as the power sector undergoes transformation.